Ollama Token Context Limit: What Happens When You Exceed It?

8/12/2025

So, You Hit the Wall: What Happens When You Exceed the Token Context Limit in Ollama?

Hey there! If you're diving into the world of running large language models (LLMs) locally with Ollama, you're on a pretty exciting journey. It's AWESOME to have that kind of power on your own machine, right? But as you start pushing the boundaries, you've probably wondered, or maybe even frustratingly discovered, that these models have their limits. Specifically, a "token context limit" or "context window".

It's a common hurdle. You're having a great, long conversation with a model, or you're trying to get it to summarize a hefty document, & then... it starts acting weird. It forgets what you were talking about, the summary is incomplete, or the quality of the responses just nose-dives.

Honestly, it's one of the most important things to get your head around when you're working with LLMs. So, let's break down what's actually happening under the hood in Ollama when you push past that limit, & more importantly, what you can do about it.

First Off, What's a Token & a Context Limit Anyway?

Before we get into the nitty-gritty of what breaks, let's make sure we're on the same page.

Think of tokens as the building blocks of language for an LLM. They're not exactly words, but chunks of text. A common word like "apple" might be one token, but a more complex word like "unbelievably" might be broken down into "un" & "believably" – two tokens. On average, for English text, you can think of a token as being about 4 characters, or roughly 75 words per 100 tokens.

Now, the token context limit, or context window, is like the model's short-term memory. It's the maximum number of tokens the model can look at at one time. This includes both your input (your prompt, your questions, the document you fed it) AND the model's output (its answer).

So, if a model has a 4,096-token context window, it can only consider a total of 4,096 tokens of conversation history, instructions, & its own generated response at any given moment. Anything outside that window is, well, forgotten.

The Big Question: What Happens When You Go Over the Limit in Ollama?

So you've been chatting with a Llama 3 model, feeding it a bunch of information, & your conversation history plus your new prompt exceeds its context window. What happens next? Does it crash? Does it throw a big, scary error message?

Turns out, the most common behavior is something a lot more subtle & potentially confusing: silent truncation.

Basically, Ollama (and the frameworks it's built on) will start to discard the oldest tokens from the context to make room for the new ones. It's like a "first-in, first-out" system. The beginning of your conversation or the start of a long document just gets… dropped.

Here's a breakdown of the consequences:

Loss of Context & "Amnesia": This is the most obvious one. The model will literally forget what you talked about earlier. You might have given it a detailed set of instructions or a persona to adopt at the start of the conversation, but if you exceed the context limit, that initial prompt could be the first thing to go. Suddenly, the bot's personality changes, or it asks you for information you've already provided.
Incomplete Analysis: If you're trying to summarize a large document, this is a HUGE problem. The model might only "see" the last part of the document, leading to a summary that's completely missing the introduction & key arguments from the beginning. This can make the output misleading or just plain wrong.
Degraded Response Quality: When a model loses context, its ability to generate coherent & relevant responses plummets. It might start repeating itself, going off on tangents, or providing generic answers because it no longer has the rich context it needs to have a nuanced conversation.
Potential for Errors (But It's Less Common): While silent truncation is the usual suspect, in some specific implementations or when using certain libraries with Ollama, you might actually get an error message. You might see something like
1Token indices sequence length is longer than the specified maximum sequence length for this model
. But don't count on this; more often than not, the model will just silently fail.

This is a really important thing to understand, especially if you're building any kind of application on top of Ollama. For example, if you're using it to power a customer service chatbot, you can't have it forgetting a customer's issue halfway through the conversation. That's a recipe for a terrible user experience.

This is where having a robust platform becomes critical. For businesses that need reliable AI-powered customer interactions, you can't just hope the context window is big enough. That's why solutions like Arsturn are so valuable. When you build a custom AI chatbot with Arsturn, it's designed to handle long conversations & maintain context effectively. It's trained on your specific business data, so it doesn't just have a generic memory; it has a deep understanding of your products, services, & customer needs, ensuring consistent & accurate support 24/7.

How to Check & Increase the Context Window in Ollama

The good news is, you're not stuck with the default context window! Many of the models available through Ollama support MUCH larger context windows than what's configured out of the box. For instance, a model might default to a 2048 or 4096 token window, but it's actually capable of handling 8k, 32k, or even more.

So, how do you take the training wheels off?

Step 1: Check the Current Context Limit

First, you need to see what you're working with. You can do this with a simple command in your terminal. Just pop it open & type: